Torsten Heinrich

Institutional Inertia and Institutional Change in an Expanding Normal-Form Game

We investigate aspects of institutional change in an evolutionary game-theoretic framework, in principle focusing on problems of coordination in groups when new solutions to a problem become available. In an evolutionary game with an underlying dilemma structure, we let a number of new strategies become gradually available to the agents. The dilemma structure of the situation is not changed by these. Older strategies offer a lesser payoff than newly available ones. The problem that agents have to solve for realizing improved results is, therefore, to coordinate on newly available strategies. Strategies are taken to represent institutions; the coordination on a new strategy by agents, hence, represents a change in the institutional framework of a group. The simulations we run show a stable pattern regarding such institutional changes. A number of institutions are found to coexist, with the specific number depending on the relation of payoffs achievable through the coordination of different strategies. Usually, the strategies leading to the highest possible payoff are not among these. This can be taken to reflect the heterogeneity of rules in larger groups, with different subgroups showing different behavior patterns.

Coordination on “Meso”-Levels: On the Co-evolution of Institutions, Networks and Platform Size

Until recently the “meso” level, located somehow between the “micro” and “macro” layers, has been neglected as a theoretical level in economic theory. There has been a long tradition of institutional economics though, which implicitly operates at this level, focusing on informal institutions that emerge as “aggregate” structures from direct interdependence and interaction of individual micro-level agents solving social coordination or dilemma problems. But it was not before the rise of more formal evolutionary modeling and simulation in the last 3 decades that the discussion of emergent structure at “meso”-levels has gained momentum. In the current paper, we discuss the emergence of cooperation as a social institution on a “meso”-sized platform. Departing from a commonly known situation of non-cooperative game theory, i.e., the repeated prisoners’ dilemma, cooperating agents learn to coordinate and cooperate while (and by) forming a “meso”-sized platform – the institution’s carrier group – giving rise to a process of co-evolution of (1) the institution of cooperation solving the dilemma, (2) a network of cooperating groups above minimum size but below maximum size, and (3) a better performance of the cooperating part of the whole population. An agent-based computer-simulation is used to analyze the factors contributing to this process (neighborhood structure, memory, monitoring, partner selection, incentive structure). The effects of these factors are confirmed and partially quantified. Starting with a review of the recent literature on the economics of “meso” (section “Size and ‘Meso’ Size in the Literature so far”), we proceed to analyze the co-evolution of institutional cooperation (section “Population Size in a Static ‘Single-Shot’ Perspective”) and the “meso”-sized carrier structures in a supergame of repeated games (section “The Population Perspective, More Complex Mechanisms, and Minimum and Maximum Critical Sizes”). In sections “The Population Perspective and the ‘Minimum Critical Mass’: A Graphical Display” and “‘Loosening’ Connectivity: ‘Contingent Trust’, Monitoring, Memorizing, Reputation, and Selection: With some Numerical Examples”, we add stochastic analysis, and section “Insights from Computer Simulation: An Agent-Based Model” considers an agent-based computer simulation, followed by section “A Real-World Example: Trust Polls, Country Size, and Macro-Performance” which reviews some empirical example of small and well-structured vs. large and ill-structured countries (“varieties of capitalism”). In the final section “Conclusions”, we summarize and conclude.

Evolution-Based Approaches in Economics and Evolutionary Loss of Information

Abstract: Evolutionary economics provides a self-organizing, stabilizing mechanism without relying on mechanic equilibria. However, there are substantial differences between the genetic evolutionary biology and the evolution of institutions, firms, routines, or strategies in economics. Most importantly, there is no genetic codification and no sexual reproduction in economic evolution, and the involved agents can interfere consciously and purposefully. This entails a general lack of fixation and a quick loss of information through a Muller’s ratchet-like mechanism. The present contribution discusses the analogy of evolution in biology and economics, and considers potential problems resulting in evolutionary models in economics.

Dynamics, Complexity, Evolution, and Emergence—The Roles of Game Theory and Simulation Methods

This chapter addresses the question how the different methods presented in the preceding chapters, such as simulation, complexity, game theory, and evolutionary game theory relate together. Formal definitions of complexity and emergence are introduced alongside with a short overview over the history of these concepts. A number of related terms including dynamic systems, fixed points, attractors, repellers, ergodicity, limit cycles, fractals, scale-free distributions, deterministic chaos, and bifurcations are also formally defined. The introduction to dynamic systems as given in Chapter 10 is extended to systems of differential equations and nonlinear dynamic systems, an understanding of which is required before approaching a number of the concepts addressed in the chapter. Further, a number of illustrative economic examples including replicator dynamic competition models, gambler’s ruin processes, and the Marx–Keynes–Goodwin growth cycle model are presented. The chapter also addresses the question of the modeling of complex systems.

The Narrow and the Broad Approach to Evolutionary Modeling in Economics

Abstract: Some models in evolutionary economics rely on direct analogies to genetic evolution, assuming a population of firms with routines, technologies, and strategies on which forces of diversity generation and selection act. This narrow conception can build upon previous findings from evolutionary biology. Broader concepts of evolution allow many or just one adaptive entity, instead of necessarily requiring a population. Thus, an institution or a society can also be understood as an evolutionary entity. Both the narrow and broad approaches have been extensively used in the literature, albeit in different literature traditions. I provide an overview of the conception and development of both approaches to evolutionary modeling, and argue that a generalization is needed to realize the full potential of evolutionary modeling.

The Foundations of Non-equilibrium Economics

written by French and Austrian scholars (corporative practices appear to work relatively well in small countries such as Austria). While in these countries of the continent there is a feeling of a need for reform, there is a certain distrust in the healing power of this mix of factors. What is worrying in the success of flexicurity on the European agenda is that it cannot be a substitute of ‘‘social Europe’’ in the EU Commission programs. It appears as a vision too affected by the simplified variables used in contemporary micro-econometric models, where the need to quantify and resume institutional factors in simple variables as employment protection omits or overlooks the real content of labor protection rights. The book helps the reader to understand such complexity. In particular, we should insist that labor rights are a set of evolved institutions, made by compromises between conflicting interests, giving an answer to problems of justice in labor relations. Besides assuring some human dignity to the laborer, they also interact with organization and technology to produce viable forms of production. Reforms based on simple visions would obtain some undesired result.

The Rate of Change in Evolutionary Systems and Evolutionary Economic Modeling

Abstract: Evolutionary economics seeks to model socio-economic reality as an evolutionary system. This powerful approach entails the implication of the continuous loss of information through the evolutionary process. The implication corresponds to evolutionary biology, although the systems in evolutionary economics are different from those in evolutionary biology. The issue of the loss of information has not been extensively studied in economics. Many open questions remain: Which knowledge is lost under what circumstances? Can loss of information be harmful to the socio-economic system as a whole in the presence of runaway dynamics caused by, for example, network externalities? How can the development of knowledge in economic systems be studied? The present article examines these questions and more.

Commodity and resource ETF trading patterns during the financial crisis

This study estimates the parameters of a power law fit of the distribution of log returns of exchange traded funds (ETFs) before, during, and after the recent financial crisis. It is found, that there is considerable variation both between ETFs and between calm and turbulent phases. Exponents of the daily log return distribution are estimated to lie mostly between 3.0 and 5.0 depending on the ETF. In minute-by-minute, trading data much lower power law exponents have been found concentrating between 3.0 and 4.0 and sometimes dropping to values close to or below 3.0. Further, there is evidence for changes in the distribution during times of turbulence (value of the exponent, improvement in the goodness of fit measures of the distribution). It can be hypothesized that effects such as, infinite variance (for α < 3) or changes in the form of the distribution can occur, in turn affecting the predictability of the system which has implications for the possibility to control or regulate financial markets under such conditions.

Introduction to the Microeconomics of Complex Economies

This is an introduction to direct interdependence among agents and resulting complexity microeconomics for first- and second-semester levels. It introduces to more or less complex multipersonal decision structures and sorts them after degrees of conflict. It explains basic perspectives on the microfoundations of coordination, the invisible-hand and the fallacy-of-aggregation paradigms. It explains rationality under conditions of strategic uncertainty, characterizes resulting processes as path-dependent, nonergodic, cumulative, and open-ended change, with multiple equilibria. Furthermore, it introduces social rules and institutions as solutions of repeated coordination and social dilemma games, respectively, i.e., not only rationally adopted but also partly habituated and semiconsciously applied, as long as conditions remain the same and no surprise warrants a change of strategy. Coordination and dilemma problems and rules and institutions are introduced as systems of externalities with a public good character. Finally, institutions are explained as instrumental (problem-solving) devices and also as potentially degenerated, i.e., ceremonially warranted behavioral lock-ins.

The Ideal Neoclassical Market and General Equilibrium

This chapter presents the core of neoclassical economic theory, the polypolistic competition model. The calculation of optimal consumer decisions is explained, as is the optimal calculation for companies. Partial market equilibrium and general equilibrium conditions are shown. The conditions for the general equilibrium to exist as a unique and stable equilibrium are described. Some further developments such as intertemporal optimizations are explained. Finally, some extensions in models are shown that include some slight modifications of assumptions, which result in at times substantial differences in results.